• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.151-159
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• 2021

In this article, our interest is to estimate the association between consecutive gap times which are subject to interval censoring. Such data are referred as doubly interval censored data (Sun, 2006). In a context of serial event, an induced dependent censoring frequently occurs, resulting in biased estimates. In this study, our goal is to propose a Kendall's 𝜏 based association measure for doubly interval censored data. For adjusting the impact of induced dependent censoring, the inverse probability censoring weighting (IPCW) technique is implemented. Furthermore, a multiple imputation technique is applied to recover unknown failure times owing to interval censoring. Simulation studies demonstrate that the suggested association estimator performs well with moderate sample sizes. The proposed method is applied to a dataset of children's dental records.

• The Korean Journal of Applied Statistics
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• v.37 no.6
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• pp.769-782
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• 2024

In many biomedical fields, especially in studies of disease progressions, we frequently encounter two sequential events, both of which are often interval-censored due to regular examinations. Such a structure is called doubly interval-censoring (DIC), and our primary interest is the elapsed time between two consecutive events. In this paper, we propose a weighted rank regression approach for DIC data under the semiparametric accelerated failure time model. After transforming DIC data into simple interval-censored data where the true elapsed times may lie, we develop estimation procedures with a Gehan-type weight by gathering all comparable pairs of observed residuals from transformed data. Moreover, we generalize this approach with data-dependent weights and extend it to clustered DIC data, where the cluster size is potentially informative, using an inverse weighting strategy. An efficient technique for variance estimation as an alternative to resampling techniques is considered. We establish asymptotic properties and conduct numerical studies to demonstrate finite sample performances. Finally, we illustrate our method with a real dataset for clustered DIC data.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.343-353
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• 2016

In doubly-censored data, an originating event time and a terminating event time are interval-censored. In certain analyses of such data, a researcher might be interested in the elapsed time between the originating and terminating events as well as regression modeling with risk factors. Therefore, in this study, we introduce a model evaluation method to measure the predictive ability of a model based on negative predictive values. We use a semiparametric estimate of the predictive accuracy to provide a simple and flexible method for model evaluation of doubly-censored survival outcomes. Additionally, we used simulation studies and tested data from a prostate cancer trial to illustrate the practical advantages of our approach. We believe that this method could be widely used to build prediction models or nomograms.